Removal efficiency of PM2.5 and PM10 by four types of masks

3. Materials and Methods

3.1 Data Cleanup

Over a period of 45 days, about 65,000+ data points were collected by each sensor. The data were cleaned up by removing single peaks using pandas libraries. After 10 rounds, 3.4% of the total rows were removed from the dataset with mask (referred as the mask data), and 1.7% of the total rows were removed from the dataset of the background concentration (referred as the background data). Fig 1 shows the data before and after clean up.

Fig. 3: Data cleaning/wrangle of the mask and background datasets. A lapse in data with mask shows the experiment with mask was suspended.

The PMS7003 with 1-minute interval sampling yielded large datasets. Single peaks were shown in Fig. 4 indicated sudden busts in concentration with no clear reason behind. The author accepted that the peaks as the artifact of low-cost sensors and decided to remove those peaks before analyzing the effectiveness of mask to screen out PMs.

3.2 Removal efficiency (RE)

After peaks were removed, the cleaned dataset was segmented into a pair for each experiments. One included a background and a filtering period. The background period lasted for 90 to 120 minutes to cross check the PM sensor for monitoring filtered air with the PM sensor monitoring the ambient air, also called background PM concentration.

The removal efficiency (RE) was as the portion of PM filtered out to the total PM contained in incoming air. Each of the graphs below presented the average RE, RE calculated for the crosscheck windows, and of PM2.5 and PM10 during the experiments.The results are presented in Figs. 4-7. For the boxplot graph, each boxplot marked the medium, 25th, 75th percentile and the outliers that were processed by seaborn library on the top of Matplotlib of Python package.

all masks removal
Summary of PM2.5 Removal of all tested masks presented as the average values taking all values into consideration. Details of each test presented belows with built-in software function to exclude outliers. The result is a post-analysis of this one and follow-up experiments.

background removal
Fig. 4: Removal efficiency during 3-hour crosscheck before experiments. The upper and lower of the box represent the 25th and 75th percentile with the cross inside the box is for 50th percentile (median). The lower and upper whiskers represent the minimum and maxium of the set after removing outliners which are represented by the dots.
Fig. 5: Removal efficiency of PM2.5 by each mask.
Fig. 6: Removal efficiency of PM10 by each mask.

Details of the removal during crosscheck, of PM2.5 and PM10 with fan duty are listed in Table 2. Fan duty indicates the speed of the fan which 1 as on all the time, and 0.5 is 50% on. The fan duty was used as the surrogate for the air flow through one mask.

Removal during the crosscheck is closed to zero as shown Fig. 4 and Table 2 confirmed that the setup is adequate to compare the PMs concentration of the filtered air and the background. Because the numbers are closed to zero and much smaller than the standard deviation, the RE of PMs was reported without deducting the difference during the crosschecks.

Table 2: Removal effiency during the cross check, and of PM2.5 and PM10 including all data (not excluding the outliners as shown by seaborn boxplot).
Group ID Crosscheck PM2.5 PM10 Fan Duty
Fabric FU1 -0.01 0.13±0.07 0.14±0.08 0.58
FU2 test #1 0.01 0.09±0.06 0.08±0.05 0.63
FU2 test #2 0.01 0.15±0.09 0.12±0.01 0.51
FN 0.00 0.11±0.06 0.12±0.07 0.53
Surgical S1 -0.02 0.29±0.12 0.29±0.13 0.58
S2 test #1 0.01 0.29±0.13 0.34±0.14 0.51
S2 test #2 0.03 0.33±0.06 0.33±0.08 0.55
S3 0.02 0.37±0.13 0.39±0.10 0.62
Air mask A1 -0.03 0.03±0.06 0.03±0.08 0.53
A2N 0.02 0.41±0.09 0.40±0.10 0.58
A2U 0.04 0.37±0.10 0.39±0.11 0.61
3M brand 3M1 0.03 0.47±0.08 0.52±0.07 0.56
3M2 test #1 0.01 0.57±0.09 0.59±0.09 0.60
3M2 test #2 0.01 0.56±0.09 0.62±0.07 0.62
3M old N/A 0.51±0.09 0.52±0.09 0.56
AQBlue AQ1 0.07 0.94±0.04 0.94±0.04 0.60
AQ2 0.09 0.92±0.05 0.92±0.04 0.62
3.3 Mask photographs

The experiment setup with a mask mounted on a PVC pile limits the scope of testing to the filter efficiency of the mask's material. The construction of each mask was presented on Figs. 7-10 with an USB-microscopic webcam.

Fig. 7: Photograph of fabric masks with a phone camera followed by two photo taken from a microscopic webcam. A long in black object is a string of hair for the scale. Human hair has a diameter of 60-70 µm.
Fig. 8: Photograph of surgical masks.
Fig. 9: Photograph of airmasks.
Fig. 10: Photograph of 3M brand particulate respirators.
Fig. 11: Photograph of AQBlue particulate respirators.

The construction of the farbic masks (F1U and FN) includes 3 layers with a cotton-like cloth in the outer layer, a thick polyester layer in the middle and the support layer in the inner layer. The middle layer is loosely packed with made of the thickness of the masks.

Surgical masks are 4-5 layers packed tightly. One middle layer is made of tiny fibers and densely packed. Two out the experiments.The results are presented in Figs. 4-6. Each boxplot marked the medium, 25th, 75th percentile and the outliers that were processed by Seaborn library on the top of Matplotlib of Python package.

In two masks purposely against particulate pollution, A1 is constructed with a single layer with randomly woven polyester. The size of pore compared to the hair string is not much different. Meanwhile, PM2.5 is about 30 times smaller. This visual supports the measurement on Table 2 as no effective to filter out PMs. The other mask (A2) is constructed with 5 layers with very fine fibers as shown in the bottom row in Fig. 9.

Brand-name particulate respirators, 3M 9001 with KN90 (3M1) and 3M Aura 9332+, are composed of 5 layers. The later model has the middle layer thick and pillow-like shape. The former model has two layers in the middle is composed of very fine fibers.

3.4. Airlow rate and RE

A mask is an equipment that is very specific in use; however, the working principle is as a filter. When airflow moves into the mask, the friction the mask layer creates turbulence around the pores. A higher airflow is translated to a shorter contact time with materials and a lower probability that the particles to be adsorb onto the fiber matrix. For the mechanism of filtering, please refer to this link for more information.

Figs. 12-15 presents a snapshot analysis for 4 candidates of each type. Those graphs provide detailed visuals. The correlation of the fan duty to RE is presented in Fig. 15. The author used the fan duty as the surrogate for flow rate indicator.

flow fabric
Fig. 12: Snapshot analysis with a fabric mask (FU2).
flow surgical
Fig. 13: Snapshot analysis with a surgical mask (S3).
flow through PM2.5
Fig. 14: Snapshot analysis with an air mask (A2N).
flow through 3M
Fig. 15: Snapshot analysis with a particulate respirator (3M2).

The results in Fig. 16 is consistent with the RE in Table 2. The fabric mask is porous to PMs that leads to marginal RE. The RE is not correlated with airflow because the friction is minimal. With surgical and air masks, a negative correlation indicates at a higher flow rate, a lower RE of PMs. The 3M Aura with FFP3 standard shows a correlation of -0.51, which is in line with the two above but the effect of airflow to the RE is smaller. This suggests improvements in 3M mask that allows a higher airlow with a less nagative on RE.

Fan to RE
Fig. 16: Correlation of fan duty to PMs removal efficiency. The correlation factor was calculated using Pandas built-in function and presented on the top of each graph.

The problem associated with PMs pollution is not singular to Vietnam. Other countries such as China and India have been experienced unhealthy levels. Exisiting blog posts and journal research are relevant to users in Vietnam to understand the basic and consider the recommendations. For example, Yu at el., 2014 found that the fitting of standard N95 mask to Chinese works is "poor". Informative blogs posts on the testing methods and performance of masks are useful for the "citizen science" approach.

3.5. Limitations

In a research journal, the limitation section is not included. Nevertheless, the goal of this study is to inform the mask users and some limitation the author should spell out.

  • Wearing a mask on the face is different than having a flat layer mounted on the PVC pile. The latter applies to this study. Fitting factor, in plain English, is how the mask fitted on the user's face to prevent leakage or shortcut of airflow. The standard tests included fitting a mask on the face and measure the number of particles of certain size inside and outside the mask. Therefore, the results in Table 2 represent the maximum removal efficiency in the testing conditions in this study.
  • Not all the mask are designed with the same forms. Surgical masks are better to remove PMs but the form is not designed for a close space around the nose. Short airflow from the sides of mask renders the RE to almost none with the ambient air. The fabric masks are better to curse around the nose, but the materials are not adequate to protect users against PMs. This emphasizes the first points. Even with a well-designed mask, how user's face features and actual wearing affects the final efficiency of removal, which is out of the scope in this study.
  • The results in this study neither support nor discard the rating by the manufacturer because of different testing conditions, equipment and procedures were applied. The author prefer a testing condition simulated the on-road condition rather with standard reagents. Choosing a brand name and wear the mask properly is one sentence summary.